Understanding Player Behaviour

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An introduction to analytics, map/reduce and DynamoDB on AWS. Slides from the 'Powering games with Amazon Web Services' event in London.

Technology

Capturing and
managing player
data is hard

S3

Input data

Code Elastic Name
MapReduce node

S3

Input data

Code Elastic Name
MapReduce node

Elastic
cluster

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Input data

Code Elastic Name
MapReduce node

HDFS

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cluster

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Input data

Code Elastic Name
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Queries
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+ BI
Via JDBC, Pig, Hive
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cluster

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Code Elastic Name Output
MapReduce node S3 + SimpleDB

Queries
HDFS
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Elastic
cluster

DynamoDB integrates
with Elastic MapReduce

Steady state Steady state
Batch processing

Click stream analysis for Best Buy

3.5 billion records
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Each day

Click stream analysis for Madden

Workﬂow time from 2 days to 8 hours

Procurement time from 2 months to 5 minutes
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Web log analysis and recommendation engine

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Understanding Player Behaviour

1. Understanding Player Behaviour

2. Player behaviour is priceless

3. Play statistics

4. Social graph

5. Monitor and iterate

6. Increase playability

7. Increase DLC sales

8. Increase advertising engagement

9. Ask questions of player behaviour

10. Ask questions of player behaviour data

11. Step 1: data collection

12. Database

13. Player data is complex

14. Player data is plentiful

15. Player data is fast moving

16. Capturing and managing player data is hard

17. Database can become bottleneck

18. DynamoDB

19. Step 2: analytics

20. Hadoop

21. Elastic MapReduce

22. Managed

23. Flexible

24. Java (or Ruby, Python etc)

25. Data warehouse

26. S3 Input data

27. S3 Input data Code Elastic MapReduce

28. S3 Input data Code Elastic Name MapReduce node

29. S3 Input data Code Elastic Name MapReduce node Elastic cluster

30. S3 Input data Code Elastic Name MapReduce node HDFS Elastic cluster

31. S3 Input data Code Elastic Name MapReduce node Queries HDFS + BI Via JDBC, Pig, Hive Elastic cluster

32. S3 Input data Code Elastic Name Output MapReduce node S3 + SimpleDB Queries HDFS + BI Via JDBC, Pig, Hive Elastic cluster

33.

34.

35.

36.

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39.

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41.

42.

43.

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45.

46.

47. DynamoDB integrates with Elastic MapReduce

48. Combine

49. HiveQL queries

50. Backup and restore

51. Data movement

52. Import/Export

53. Multipart upload

54. Multipart, parallel results delivery

55. Direct Connect

56. Scale control

57. Resize running job ﬂows

58. 14 hours Time remaining: 14 hours

59. 14 hours Time remaining: 7 hours

60. Time remaining: 3 hours

61. Balance cost and performance

62. Resize based on usage patterns

63. Steady state Steady state Batch processing

64. Perfect for Spot

65. Cluster types

66. Small

67. High memory High CPU or both

68. HPC

69. Click stream analysis for Best Buy 3.5 billion records 71 million unique cookies 1.7 million targeted ads 13 Tb of clickstream logs Each day

70. Click stream analysis for Madden Workﬂow time from 2 days to 8 hours Procurement time from 2 months to 5 minutes $13k per month 500% increase return on advertising spend

71. Web log analysis and recommendation engine $29.9 million in sales 842 million page views 434 Gb of page logs 97 million ‘favourites’

Understanding Player Behaviour

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Editor's Notes